Introduction

AutoSPARQL TBSL is a graphical user interface, which allows to answer natural language queries over RDF knowledge bases. It is based on algorithms implemented in the DL-Learner Semantic Web machine learning framework.

Requirements

• Java 7 or higher
• Maven 3 or higher
• Git
• an IPv6-capable internet connection (you can use a free IPv6 tunnel service like Miredo or Teredo if your connection does not support it natively)

Warning

AutoSPARQL TBSL is a research prototype that is not actively developed anymore. As such, getting it to work may need some effort and depends on the availability of several other online services. Feel free to create issues if you encounter problems and please share your fixes using pull requests.

Installation and Execution

1. clone the git repository
2. run ./compile and then ./run (Linux, Mac) or compile.bat and then run.bat (Windows)
   • Manually: Do mvn install -N in the folders in that order: autosparql, commons, algorithm-tbsl. Then go into autosparql-tbsl and run mvn jetty:run.

Then, go into your browser and access http://localhost:8080 and click on the link to the application.

Error Reporting

If you encounter errors, please look at the issues if the problem is already reported. If not, please create a single issue including the command line output. If the error occurs during compilation, please use ./createcompillelog instead of ./compile to create the compile log. Feel free to create issues if you encounter problems and please share your fixes using pull requests.

Adding your own Dataset

Using your own datasource instead of DBpedia or Oxford is nontrivial. It needs several days of work in addition to the time needed to familiarize yourself with the code base.

You need to:

• fork AutoSPARQL TBSL
• add your own domain dependent lexicon as an LTAG grammar at algorithm-tbsl/src/main/resources/tbsl/lexicon (see http://pub.uni-bielefeld.de/publication/2002961 and http://pub.uni-bielefeld.de/publication/2278529 as well as the existing files in that folder)
• add your knowledge base:
  1. as a local model to algorithm-tbsl/src/main/resources/models/yourmodel (preferred for small knowledge bases as it is much faster and more reliable)
  2. as a SPARQL (version 1.0 is enough) endpoint along with a SOLR server instance
• create a singleton for your knowledge base (see package org.aksw.autosparql.tbsl.algorithm.knowledgebase)
• extend org.aksw.autosparql.tbsl.algorithm.learning.TBSL with TbslYourKnowledgeBase
• finally, in case the dataset isn't a private model, please do a pull request so that it can be integrated in the main project

Modules and Packages